Believing in free will may arise from a biological need for control. People induced to disbelieve in free will show impulsive and antisocial tendencies, suggesting a reduction of the willingness to exert self-control. We investigated whether undermining free will affects two aspects of self-control: intentional inhibition and perceived self-control. We exposed participants either to anti-free will or to neutral messages. The two groups then performed a task that required self-control to inhibit a prepotent response. No-free will participants showed less intentional (...) inhibitions than controls, suggesting a reduction of self-control. We assessed perceived self-control by asking participants whether the response resulted from a deliberate intention or from an impulsive reaction. Perceived self-control was lower in the no-free will group than in control group. Our findings show that undermining free will can degrade self-control and provide insights into how disbelieving in free will leads to antisocial tendencies. (shrink)

Recent controversies about the level of replicability of behavioral research analyzed using statistical inference have cast interest in developing more efficient techniques for analyzing the results of psychological experiments. Here we claim that complementing the analytical workflow of psychological experiments with Machine Learning-based analysis will both maximize accuracy and minimize replicability issues. As compared to statistical inference, ML analysis of experimental data is model agnostic and primarily focused on prediction rather than inference. We also highlight some potential pitfalls resulting from (...) adoption of Machine Learning based experiment analysis. If not properly used it can lead to over-optimistic accuracy estimates similarly observed using statistical inference. Remedies to such pitfalls are also presented such and building model based on cross validation and the use of ensemble models. ML models are typically regarded as black boxes and we will discuss strategies aimed at rendering more transparent the predictions. (shrink)

In the present manuscript, we comment upon a paper that strongly criticized an expert report written by the consultants of the defense in a case of pedophilia, in which clinical and neuro-scientific data were used to establish the causal link between brain alterations and onset of criminal behavior. These critiques appear to be based mainly on wrong pieces of information and on a misinterpretation of the logical reasoning adopted by defense consultants. Here we provide a point-by-point reply to the issues (...) raised in the above paper and also discuss the potential role that neuroscience may contribute in the forensic context. Did the forensic neuroscience defense consultants claim the existence of a deterministic relationship between brain structure or function and behavior? How did the neuroscientific logic work in this specific case? How may the classic psychiatric/neurologic examination and neuroscientific evidence work side by side? Does the rarity of a disease impact on the causal relationship between the disease and the crime? Do neuroscientific data need to be interpreted? We address the above questions and conclude that neuroscience may strengthen the results of psychiatric evaluations, thus reducing uncertainty in the forensic settings. (shrink)

As we write this research paper, we notice an explosion in popularity of machine learning in numerous fields (ranging from governance, education, and management to criminal justice, fraud detection, and internet of things). In this contribution, rather than focusing on any of those fields, which have been well-reviewed already, we decided to concentrate on a series of more recent applications of deep learning models and technologies that have only recently gained significant track in the relevant literature. These applications are concerned (...) with artistic production (Sect. 2.1), the writing process (Sect. 2.2), music production (Sect. 2.3), text recognition and attribution (Sect. 2.4). After reviewing and analyzing the positive contributions as well as some of the major limitations of these technologies in each of those fields, we critically reflect (Sect. 3) on how their widespread implementation may affect humans and their creativity. In Sect. 4, we notice that deep learning models are here to stay; so, rather than embracing a negative or pessimistic stance with respect to their future applications in creative domains, we suggest a balanced approach for their assessment and beneficial usage. Finally (Sect. 5), we conclude by summarising what we have achieved and by pointing out possible future research directions. (shrink)